Probabilistic bus delay predictions with Bayesian networks
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Date
2021
Publication Type
Conference Paper
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yes
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Abstract
Numerous decisions of users and operators of public transport systems depend on the availability of good arrival and departure time predictions. Passengers decide on departure time, route choice, or mode choice and operators decide on schedules, timetables, rolling stock allocation, or control actions. In practice, not only the most likely value of a bus delay is of interest, but also its variability. This paper focuses on the probabilistic prediction of bus delays with realtime information. The dynamics of bus operations are modeled by a Bayesian network framework, allowing the description of the time-dependent stochastic processes of delay evolution. The model structure can capture the dependencies between bus operation, passenger ridership, and road demand. The application to urban bus lines in Zurich, Switzerland, shows an increased prediction accuracy compared with other methods. The model allows predicting the associated variability of bus delays and provides, therefore, the basis for more accurate passenger information and risk-based decisions making of operators.
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Publication status
published
Editor
Book title
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Journal / series
Volume
Pages / Article No.
3752 - 3758
Publisher
IEEE
Event
24th IEEE International Intelligent Transportation Systems Conference (ITSC 2021)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Support vector machines; Schedules; Decision making; Stochastic processes; Predictive models; Probabilistic logic; Real-time systems
Organisational unit
09611 - Corman, Francesco / Corman, Francesco
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG